I'm trying to get multiple summary statistics in R/S-PLUS grouped by categorical column in one shot. I found couple of functions, but all of them do one statistic per call, like aggregate().

data <- c(62, 60, 63, 59, 63, 67, 71, 64, 65, 66, 68, 66, 71, 67, 68, 68, 56, 62, 60, 61, 63, 64, 63, 59)
grp <- factor(rep(LETTERS[1:4], c(4,6,6,8)))
df <- data.frame(group=grp, dt=data)
mg <- aggregate(df$dt, by=df$group, FUN=mean)
mg <- aggregate(df$dt, by=df$group, FUN=sum) 

What I'm looking for is to get multiple statistics for the same group like mean, min, max, std, ...etc in one call, is that doable?

3

14 Answers

1. tapply

I'll put in my two cents for tapply().

tapply(df$dt, df$group, summary)

You could write a custom function with the specific statistics you want or format the results:

tapply(df$dt, df$group, function(x) format(summary(x), scientific = TRUE))
$A Min. 1st Qu. Median Mean 3rd Qu. Max.
"5.900e+01" "5.975e+01" "6.100e+01" "6.100e+01" "6.225e+01" "6.300e+01"
$B Min. 1st Qu. Median Mean 3rd Qu. Max.
"6.300e+01" "6.425e+01" "6.550e+01" "6.600e+01" "6.675e+01" "7.100e+01"
$C Min. 1st Qu. Median Mean 3rd Qu. Max.
"6.600e+01" "6.725e+01" "6.800e+01" "6.800e+01" "6.800e+01" "7.100e+01"
$D Min. 1st Qu. Median Mean 3rd Qu. Max.
"5.600e+01" "5.975e+01" "6.150e+01" "6.100e+01" "6.300e+01" "6.400e+01"

2. data.table

The data.table package offers a lot of helpful and fast tools for these types of operation:

library(data.table)
setDT(df)
> df[, as.list(summary(dt)), by = group] group Min. 1st Qu. Median Mean 3rd Qu. Max.
1: A 59 59.75 61.0 61 62.25 63
2: B 63 64.25 65.5 66 66.75 71
3: C 66 67.25 68.0 68 68.00 71
4: D 56 59.75 61.5 61 63.00 64

3

dplyr package could be nice alternative to this problem:

library(dplyr)
df %>% group_by(group) %>% summarize(mean = mean(dt), sum = sum(dt))

To get 1st quadrant and 3rd quadrant

df %>% group_by(group) %>% summarize(q1 = quantile(dt, 0.25), q3 = quantile(dt, 0.75))

0

Using Hadley Wickham's purrr package this is quite simple. Use split to split the passed data_frame into groups, then use map to apply the summary function to each group.

library(purrr)
df %>% split(.$group) %>% map(summary)

2

There's many different ways to go about this, but I'm partial to describeBy in the psych package:

describeBy(df$dt, df$group, mat = TRUE) 

0

take a look at the plyr package. Specifically, ddply

ddply(df, .(group), summarise, mean=mean(dt), sum=sum(dt))

0

after 5 long years I'm sure not much attention is going to be received for this answer, But still to make all options complete, here is the one with data.table

library(data.table)
setDT(df)[ , list(mean_gr = mean(dt), sum_gr = sum(dt)) , by = .(group)]
# group mean_gr sum_gr
#1: A 61 244
#2: B 66 396
#3: C 68 408
#4: D 61 488 

The psych package has a great option for grouped summary stats:

library(psych)
describeBy(dt, group="grp")

produces lots of useful stats including mean, median, range, sd, se.

Besides describeBy, the doBy package is an another option. It provides much of the functionality of SAS PROC SUMMARY. Details:

1

While some of the other approaches work, this is pretty close to what you were doing and only uses base r. If you know the aggregate command this may be more intuitive.

with( df , aggregate( dt , by=list(group) , FUN=summary) )

2

Not sure why the popular skimr package hasn’t been brought up. Their function skim() was meant to replace the base R summary() and supports dplyr grouping:

library(dplyr)
library(skimr)
starwars %>% group_by(gender) %>% skim()
#> ── Data Summary ────────────────────────
#> Values
#> Name Piped data
#> Number of rows 87
#> Number of columns 14
#> _______________________
#> Column type frequency:
#> character 7
#> list 3
#> numeric 3
#> ________________________
#> Group variables gender
#>
#> ── Variable type: character ──────────────────────────────────────────────────────
#> skim_variable gender n_missing complete_rate min max empty n_unique
#> 1 name feminine 0 1 3 18 0 17
#> 2 name masculine 0 1 3 21 0 66
#> 3 name <NA> 0 1 8 14 0 4
#> 4 hair_color feminine 0 1 4 6 0 6
#> 5 hair_color masculine 5 0.924 4 13 0 9
#> 6 hair_color <NA> 0 1 4 7 0 4
#> # [...]
#>
#> ── Variable type: list ───────────────────────────────────────────────────────────
#> skim_variable gender n_missing complete_rate n_unique min_length max_length
#> 1 films feminine 0 1 9 1 5
#> 2 films masculine 0 1 24 1 7
#> 3 films <NA> 0 1 3 1 2
#> 4 vehicles feminine 0 1 3 0 1
#> 5 vehicles masculine 0 1 9 0 2
#> 6 vehicles <NA> 0 1 1 0 0
#> # [...]
#>
#> ── Variable type: numeric ────────────────────────────────────────────────────────
#> skim_variable gender n_missing complete_rate mean sd p0 p25 p50
#> 1 height feminine 1 0.941 165. 23.6 96 162. 166.
#> 2 height masculine 4 0.939 177. 37.6 66 171. 183
#> 3 height <NA> 1 0.75 181. 2.89 178 180. 183
#> # [...]

With more recent (>1.0) versions of dplyr you can do so with

iris %>% group_by(Species) %>% summarise(as_tibble(rbind(summary(Sepal.Length))))

This works because dplyr will unpack the result of summarise into columns if the argument evaluates into a dataframe.

I would also recommend gtsummary (written by Daniel D. Sjoberg et al). You can generate publication-ready or presentation-ready tables with the package. A gtsummary solution to the example given in the question would be:

library(tidyverse)
library(gtsummary)
data <- c(62, 60, 63, 59, 63, 67, 71, 64, 65, 66, 68, 66, 71, 67, 68, 68, 56, 62, 60, 61, 63, 64, 63, 59)
grp <- factor(rep(LETTERS[1:4], c(4,6,6,8)))
df <- data.frame(group=grp, dt=data)
tbl_summary(df, by=group, type = all_continuous() ~ "continuous2", statistic = all_continuous() ~ c("{mean} ({sd})","{median} ({IQR})", "{min}- {max}"), ) %>% add_stat_label(label = dt ~ c("Mean (SD)","Median (Inter Quant. Range)", "Min- Max"))

which then gives you the output below

Characteristic	A, N = 4	B, N = 6	C, N = 6	D, N = 8
dt
Mean (SD)	61.0 (1.8)	66.0 (2.8)	68.0 (1.7)	61.0 (2.6)
Meian (IQR)	61.0 (2.5)	65.5 (2.5)	68.0 (0.8)	61.5 (3.2)
Min- Max	59.0 - 63.0	63.0 - 71.0	66.0 - 71.0	56.0 - 64.0

You can also export the table as word document by doing the following:

Table1 <- tbl_summary(df, by=group, type = all_continuous() ~ "continuous2", statistic = all_continuous() ~ c("{mean} ({sd})","{median} ({IQR})", "{min}- {max}"), ) %>% add_stat_label(label = dt ~ c("Mean (SD)","Median (Inter Quant. Range)", "Min- Max"))
tmp1 <- "~path/name.docx"
Table1 %>% as_flex_table() %>% flextable::save_as_docx(path=tmp1)

You can use it for regression outputs as well. See the package reference manual and the package webpage for further insights

First, it depends on your version of R. If you've passed 2.11, you can use aggreggate with multiple results functions(summary, by instance, or your own function). If not, you can use the answer made by Justin.

this may also work,

spl <- split(mtcars, mtcars$cyl)
list.of.summaries <- lapply(spl, function(x) data.frame(apply(x[,3:6], 2, summary)))
list.of.summaries

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